To train the QG model, we used the K-VQG dataset [1]}, which contains knowledge-aware questions about objects in images, and the newly created Professional K-VQG dataset.
The Professional K-VQG dataset contains knowledge-aware visual questions related to objects annotated by professional annotators.
The source of the images is the Visual Genome [2]}, and the sources of knowledge are ConceptNet [3]} and Atomic\(^{20}_{20}\) [4]}.
We selected 371 object classes from the Visual Genome dataset that were commonly included as heads in the knowledge sources.
In the annotation process, for each candidate object in the image, we obtained the corresponding knowledge triplets from the knowledge sources and asked the annotator to write a knowledge-aware question, with the head or tail of the knowledge triplet as the answer.
As a result, we obtained 10,431 questions for 9,210 images and 5,242 unique knowledge.
